Principles of Econometrics 3rd Edition by R Carter, William E Griffiths, Guay C Lim Hill ISBN 0471723606 9780471723608

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ISBN 10: 0471723606 
ISBN 13: 9780471723608
Author: R Carter, William E Griffiths, Guay C Lim Hill

Principles of Econometrics 3rd Table of contents:

Chapter 1: The Nature of Econometrics and Economic Data

• 1.1 What is Econometrics?
• 1.2 Steps in Econometric Analysis
• 1.3 Economic Data: Cross-Sectional, Time Series, Panel Data, Pooled Cross-Sections
• 1.4 Causality and the Ceteris Paribus Assumption
• 1.5 The Role of Econometrics in Policy and Decision Making

Chapter 2: The Simple Linear Regression Model

• 2.1 Definition of the Simple Linear Regression Model
• 2.2 The Ordinary Least Squares (OLS) Estimators
• 2.3 Properties of the OLS Estimators: The Gauss-Markov Assumptions
• 2.4 Unbiasedness and Efficiency of OLS Estimators
• 2.5 The Coefficient of Determination (R2)
• 2.6 Functional Forms of the Simple Linear Regression Model
• 2.7 Application: Estimating a Consumption Function / Wage Equation

Chapter 3: Inference in the Simple Linear Regression Model

• 3.1 Sampling Distributions of the OLS Estimators
• 3.2 Hypothesis Testing: t-tests for Individual Coefficients
• 3.3 Confidence Intervals for Regression Coefficients
• 3.4 p-Values and Statistical Significance
• 3.5 Prediction and Forecast Intervals
• 3.6 Goodness-of-Fit and the F-test for Overall Significance (Introduction)

Chapter 4: Multiple Regression Analysis: Estimation

• 4.1 Motivation for Multiple Regression
• 4.2 The Multiple Regression Model: Interpretation of Coefficients
• 4.3 OLS Estimation in Multiple Regression
• 4.4 Assumptions of the Multiple Linear Regression Model
• 4.5 Properties of OLS Estimators in Multiple Regression (Gauss-Markov Theorem)
• 4.6 Goodness-of-Fit: Adjusted R2
• 4.7 Matrix Approach to Multiple Regression (Optional/Appendix)

Chapter 5: Multiple Regression Analysis: Inference

• 5.1 Hypothesis Testing about Individual Population Parameters (t-tests)
• 5.2 Confidence Intervals for Population Parameters
• 5.3 Testing Multiple Linear Restrictions: The F-test
• 5.4 Testing for Overall Significance of the Regression
• 5.5 Using Restricted and Unrestricted Models for F-tests
• 5.6 General Linear Restrictions
• 5.7 Prediction with Multiple Regression

Chapter 6: Dummy Variables

• 6.1 Incorporating Qualitative Information in Regression Models
• 6.2 A Single Dummy Variable
• 6.3 Using Dummy Variables for Multiple Categories
• 6.4 Dummy Variable Trap
• 6.5 Interacting Dummy Variables
• 6.6 Interaction Terms with Quantitative Variables
• 6.7 Applications: Gender Wage Gap, Policy Analysis

Chapter 7: Model Specification and Data Problems

• 7.1 Consequences of Misspecification: Omitted Variable Bias
• 7.2 Consequences of Including Irrelevant Variables
• 7.3 Functional Form Misspecification: Logarithms, Quadratics
• 7.4 Reset Test for Functional Form
• 7.5 Proxy Variables for Unobservable Regressors
• 7.6 Measurement Error
• 7.7 Data Scaling and its Effects on OLS Statistics

Chapter 8: Multicollinearity

• 8.1 What is Multicollinearity? Perfect vs. Imperfect
• 8.2 Consequences of Multicollinearity
• 8.3 Detecting Multicollinearity: Variance Inflation Factor (VIF)
• 8.4 Solutions to Multicollinearity (if problematic)

Chapter 9: Heteroskedasticity

• 9.1 Nature of Heteroskedasticity
• 9.2 Consequences of Heteroskedasticity for OLS
• 9.3 Detecting Heteroskedasticity: Graphical Methods, Breusch-Pagan Test, White Test
• 9.4 Remedial Measures: Weighted Least Squares (WLS)
• 9.5 Robust Standard Errors (White’s Heteroskedasticity-Consistent Estimators)

Chapter 10: Autocorrelation (Serial Correlation)

• 10.1 Nature of Autocorrelation: First-Order, Higher-Order
• 10.2 Consequences of Autocorrelation for OLS
• 10.3 Detecting Autocorrelation: Durbin-Watson Test, Breusch-Godfrey Test
• 10.4 Remedial Measures: Generalized Least Squares (GLS)
• 10.5 Newey-West Standard Errors (HAC Estimators)
• 10.6 Forecasting with Autocorrelated Errors

Chapter 11: Introduction to Time Series Analysis

• 11.1 Basic Concepts of Time Series Data
• 11.2 Stationarity and Non-Stationarity
• 11.3 Autoregressive (AR) and Moving Average (MA) Processes
• 11.4 Autoregressive Moving Average (ARMA) Models
• 11.5 Unit Root Tests (Dickey-Fuller)
• 11.6 Cointegration (Brief Introduction)
• 11.7 Vector Autoregressions (VARs) (Brief Introduction)

Chapter 12: Panel Data Models

• 12.1 Advantages of Panel Data
• 12.2 Pooled OLS
• 12.3 Fixed Effects Model (Within Estimator)
• 12.4 Random Effects Model
• 12.5 Choosing Between Fixed and Random Effects (Hausman Test)
• 12.6 Dynamic Panel Data Models (Brief Introduction)

Chapter 13: Limited Dependent Variable Models

• 13.1 Binary Response Models: Probit and Logit
• 13.2 Estimation and Interpretation of Probit and Logit Models
• 13.3 Multinomial Logit and Probit (Brief Introduction)
• 13.4 Ordered Response Models
• 13.5 Censored Regression Models (Tobit)
• 13.6 Truncated Regression Models
• 13.7 Count Data Models (Poisson Regression)

Chapter 14: Simultaneous Equations Models

• 14.1 Endogeneity Bias in Single Equation Models
• 14.2 Structure of Simultaneous Equations Models
• 14.3 Identification Problem
• 14.4 Estimation Methods: Two-Stage Least Squares (2SLS)
• 14.5 Instrumental Variables (IV) Estimation

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